Glaucoma is a chronic eye condition in which the nerve that connects the eye to the brain (optic nerve) is progressively damaged. Patients with early stages of glaucoma do not have visual symptoms. Progression of the glaucoma results in loss of peripheral vision, so patients may complain of vision loss. Although glaucoma cannot be cured, treatments can slow down the progression of the disease. Thus, early detection of glaucoma is critical and beneficial to patients. However, routine screening for glaucoma for the whole population is limited by poor sensitivity of current tests. Screening may be useful for high risk individuals, such as first degree relatives of a glaucoma patient, older individuals (e.g. 65 years and older) and elderly Chinese women (who are at risk of angle closure glaucoma).
There are three current methods to detect glaucoma:    (1) Assessment of raised intraocular pressure (IOP);    (2) Assessment of abnormal visual field; and    (3) Assessment of damage to the optic nerve.
IOP measurement is neither specific nor sensitive enough to be an effective screening tool. Visual field testing requires special equipment only present in tertiary hospitals. Moreover, visual field testing is not sensitive enough to detect early glaucoma damage as these patients do not have visual symptoms.
Assessment of damage to the optic nerve is more promising and superior to IOP or visual field testing. It is performed using a fundus image (that is a two-dimensional image of the rear of the eye composed of intensity values for each of the pixels of the image; there are typically multiple intensity values for each pixel, corresponding to different colour channels). Optic nerve assessment can be done by a trained specialist (ophthalmologist). However, the technique involves manual assessment of the optic disc assessment, which is subjective, and the cost of training an ophthalmologist is high. It is beneficial to develop tools to automatically analyse the optic disc from fundus images for glaucoma screening. The optic disc (OD) is the location where ganglion cell axons exit the eye to form the optic nerve. The localisation and segmentation of OD is very important in many computer aided diagnosis systems such as glaucoma screening. The localisation focuses on finding an OD pixel, very often the centre of the OD. The segmentation estimates the OD boundary. FIG. 1(a) and (d) are two fundus images. FIGS. 1(b) and (e) are derived from FIG. 1(a) and (d) respectively, and the lines marked 1 are the ground truth OD boundaries.
Conventional approaches proposed for. OD segmentation include template-based methods, deformable model-based methods and pixel classification based methods. The first two types of method are on the basis of the edge characteristics. The performance very much depends on the differentiation of edges from OD and other structures especially peripapillary atrophy (PPA), which is present in the area between the lines marked 1 and 2 in FIG. 1(b). As PPA looks similar to OD, it is often mistaken for part of OD. For example, the lines marked 3 in FIG. 1 are the boundary detected by existing OD segmentation algorithms. Deformable models are sensitive to poor initialization. Very often, the deformation cannot exclude PPA from the segmented OD if it has been included in the initialization. A study compared the deformable model-based methods using an active contour with pixel classification based methods and concluded that their performances were similar especially for images without PPA or other pathologies. The results also show that the features used in the pixel classification-based methods were not very effective in differentiating PPA region from OD. Therefore, PPA is the main challenge in performing OD segmentation accurately in all three types of methods. Moreover, pixels are not natural entities and the number of pixels is high even at moderate resolutions, which makes optimization of the level of pixels intractable in pixel classification methods.
A common limitation of existing methods is that they do not generate a measure of the reliability of the segmented result, i.e., these methods give a segmentation without any knowledge of how good or reliable the result is. Thus, the system might produce bad segmentation results without a warning. When the segmented OD is used for further processing, for example, cup segmentation for cup to disc ratio based glaucoma screening, the errors would propagate.
On 12 Oct. 2012, some of the present inventors filed a U.S. patent application Ser. No. 13/651,309, entitled “Methods and Systems for Detecting Peripapillary Atrophy”, which was unpublished as of the priority date of the present application. It suggests a method for detecting PPA in which a region of interest in a fundus image is divided into sub-regions, biologically-inspired features (BIF) are extracted for the sub-regions, and an adaptive model is used to generate data indicating whether PPA is present.